comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/mk_map_hhmm.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function bnet = mk_map_hhmm(varargin)
+
+% p is the prob of a successful move (defines the reliability of motors)
+p = 1;
+obs_model = 'unique';
+
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'p', p = varargin{i+1};
+   case 'obs_model', obs_model = varargin{i+1};
+  end
+end
+
+
+q = 1-p;
+unique_obs = strcmp(obs_model, 'unique');
+
+% assign numbers to the nodes in topological order
+U = 1; A = 2; C = 3; F = 4;
+if unique_obs
+  onodes = 5;
+else
+  N = 5; E = 6; S = 7; W = 8; % north, east, south, west
+  onodes = [N E S W];
+end
+
+% create graph structure
+
+ss = 4 + length(onodes); % slice size
+intra = zeros(ss,ss);
+intra(U,F)=1;
+intra(A,[C F onodes])=1;
+intra(C,[F onodes])=1;
+
+inter = zeros(ss,ss);
+inter(U,[A C])=1;
+inter(A,[A C])=1;
+inter(F,[A C])=1;
+inter(C,C)=1;
+
+% node sizes
+ns = zeros(1,ss);
+ns(U) = 2; % left/right
+ns(A) = 2;
+ns(C) = 3;
+ns(F) = 2;
+if unique_obs
+  ns(onodes) = 5; % we will assign each state a unique symbol
+else
+  ns(onodes) = 2;
+end
+l = 1; r = 2; % left/right
+L = 1; R = 2;
+
+% Make the DBN
+bnet = mk_dbn(intra, inter, ns, 'observed', onodes);
+eclass = bnet.equiv_class;
+
+
+
+% Define CPDs for slice 1
+% We clamp all the CPDs that are not tied,
+% since we cannot learn them from a single sequence.
+
+% uniform probs over actions (the input could be chosen from a policy)
+bnet.CPD{eclass(U,1)} = tabular_CPD(bnet, U, 'CPT', mk_stochastic(ones(ns(U),1)), ...
+				    'adjustable', 0);
+
+% uniform probs over starting abstract state
+bnet.CPD{eclass(A,1)} = tabular_CPD(bnet, A, 'CPT', mk_stochastic(ones(ns(A),1)), ...
+				    'adjustable', 0);
+
+% Uniform probs over starting concrete state, modulo the fact
+% that corridor 2 is only of length 2.
+CPT = zeros(ns(A), ns(C)); % CPT(i,j) = P(C starts in j | A=i)
+CPT(1, :) = [1/3 1/3 1/3];
+CPT(2, :) = [1/2 1/2 0];
+bnet.CPD{eclass(C,1)} = tabular_CPD(bnet, C, 'CPT', CPT, 'adjustable', 0);
+
+% Termination probs
+CPT = zeros(ns(U), ns(A), ns(C), ns(F));
+CPT(r,1,1,:) = [1 0];
+CPT(r,1,2,:) = [1 0];
+CPT(r,1,3,:) = [q p];
+CPT(r,2,1,:) = [1 0];
+CPT(r,2,2,:) = [q p];
+CPT(l,1,1,:) = [q p];
+CPT(l,1,2,:) = [1 0];
+CPT(l,1,3,:) = [1 0];
+CPT(l,2,1,:) = [q p];
+CPT(l,2,2,:) = [1 0];
+
+bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', CPT);
+
+
+% Observation model
+if unique_obs
+  CPT = zeros(ns(A), ns(C), 5);
+  CPT(1,1,1)=1;  % Theo state 4
+  CPT(1,2,2)=1;  % Theo state 5
+  CPT(1,3,3)=1; % Theo state 6
+  CPT(2,1,4)=1; % Theo state 9
+  CPT(2,2,5)=1; % Theo state 10
+  %CPT(2,3,:) undefined
+  O = onodes(1);
+  bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', CPT);
+else
+  % north/east/south/west can see wall (1) or opening (2)
+  CPT = zeros(ns(A), ns(C), 2);
+  CPT(:,:,1) = q;
+  CPT(:,:,2) = p;
+  bnet.CPD{eclass(W,1)} = tabular_CPD(bnet, W, 'CPT', CPT);
+  bnet.CPD{eclass(E,1)} = tabular_CPD(bnet, E, 'CPT', CPT);
+  CPT = zeros(ns(A), ns(C), 2);
+  CPT(:,:,1) = p;
+  CPT(:,:,2) = q;
+  bnet.CPD{eclass(S,1)} = tabular_CPD(bnet, S, 'CPT', CPT);
+  bnet.CPD{eclass(N,1)} = tabular_CPD(bnet, N, 'CPT', CPT);
+end
+
+% Define the CPDs for slice 2
+
+% Abstract
+
+% Since the top level never resets, the starting distribution is irrelevant:
+% A2 will be determined by sampling from transmat(A1,:).
+% But the code requires we specify it anyway; we make it all 0s, a dummy value.
+startprob = zeros(ns(U), ns(A));
+
+transmat = zeros(ns(U), ns(A), ns(A));
+transmat(R,1,:) = [q p];
+transmat(R,2,:) = [0 1];
+transmat(L,1,:) = [1 0];
+transmat(L,2,:) = [p q];
+
+% Qps are the parents we condition the parameters on, in this case just
+% the past action.
+bnet.CPD{eclass(A,2)} = hhmm2Q_CPD(bnet, A+ss, 'Fbelow', F, ...
+				  'startprob', startprob, 'transprob', transmat);
+
+
+
+% Concrete
+
+transmat = zeros(ns(C), ns(U), ns(A), ns(C));
+transmat(1,r,1,:) = [q p 0.0];
+transmat(2,r,1,:) = [0.0 q p];
+transmat(3,r,1,:) = [0.0 0.0 1.0];
+transmat(1,r,2,:) = [q p 0.0];
+transmat(2,r,2,:) = [0.0 1.0 0.0];
+%
+transmat(1,l,1,:) = [1.0 0.0 0.0];
+transmat(2,l,1,:) = [p q 0.0];
+transmat(3,l,1,:) = [0.0 p q];
+transmat(1,l,2,:) = [1.0 0.0 0.0];
+transmat(2,l,2,:) = [p q 0.0];
+
+% Add a new dimension for A(t-1), by copying old vals,
+% so the matrix is the same size as startprob
+
+
+transmat = reshape(transmat, [ns(C) ns(U) ns(A) 1 ns(C)]);
+transmat = repmat(transmat, [1 1 1 ns(A) 1]);
+
+% startprob(C(t-1), U(t-1), A(t-1), A(t), C(t))
+startprob = zeros(ns(C), ns(U), ns(A), ns(A), ns(C));
+startprob(1,L,1,1,:) = [1.0 0.0 0.0];
+startprob(3,R,1,2,:) = [1.0 0.0 0.0];
+startprob(3,R,1,1,:) = [0.0 0.0 1.0];
+% 
+startprob(1,L,2,1,:) = [0.0 0.0 010];
+startprob(2,L,2,1,:) = [1.0 0.0 0.0];
+startprob(2,R,2,2,:) = [0.0 1.0 0.0];
+
+% want transmat(U,A,C,At,Ct), ie. in topo order
+transmat = permute(transmat, [2 3 1 4 5]);
+startprob  = permute(startprob, [2 3 1 4 5]);
+bnet.CPD{eclass(C,2)} = hhmm2Q_CPD(bnet, C+ss, 'Fself', F, ...
+				  'startprob', startprob, 'transprob', transmat);
+
+